MétaCan
Menu
Back to cohort

Cyclic behaviour of CLT shear wall hold-down connections using mixed angle self-tapping screws

2023· article· en· W4366085626 on OpenAlexaff
Thomas Wright, Minghao Li, Daniel Moroder, Hyungsuk Lim, David Carradine

Bibliographic record

VenueEngineering Structures · 2023
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
FundersEarthquake Commission
KeywordsConnection (principal bundle)StiffnessStructural engineeringDuctility (Earth science)Materials scienceTappingComposite materialShear (geology)EngineeringMechanical engineeringCreep

Abstract

fetched live from OpenAlex

This paper experimentally examines structural performance of novel cross laminated timber (CLT) hold-down connections consisting of customised steel brackets and self-tapping screws installed with mixed angles relative to loading. The installation of screws at mixed angles combines the high strength and stiffness of inclined screws with the high ductility of 90° screws creating a strong, stiff, and ductile connection. A total of 30 high capacity connection specimens comprising two timber species and five connection configurations are tested under monotonic and cyclic loading. The ratios between inclined screws and 90° screws are investigated in order to optimise the connection seismic performance. Connections consisting of 12× Ø12 mm inclined screws and 12 to 24× Ø12mm / 10 mm 90° screws were found to demonstrate high ultimate strength (470–593 kN), high initial stiffness (212–269 kN/mm) and high ductility (µ=10–20) under cyclic loading. Current strength calculation methods for the connection design are compared to the experimental results and are shown to provide conservative design predictions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.214
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEngineering StructuresSame topicWood Treatment and PropertiesFrench-language works237,207